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R Programming: Advanced Analytics In R For Data Science by Udemy
Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2
Course Highlights
- Perform Data Preparation in R
- Identify missing records in dataframes
- Locate missing data in your dataframes
- Apply the Median Imputation method to replace missing records
- Apply the Factual Analysis method to replace missing records
- Understand how to use the which() function
- Know how to reset the dataframe index
- Work with the gsub() and sub() functions for replacing strings
- Explain why NA is a third type of logical constant
- Deal with date-times in R
- Convert date-times into POSIXct time format
- Create, use, append, modify, rename, access and subset Lists in R
- Understand when to use [] and when to use [[]] or the $ sign when working with Lists
- Create a timeseries plot in R
- Understand how the Apply family of functions works
- Recreate an apply statement with a for() loop
- Use apply() when working with matrices
- Use lapply() and sapply() when working with lists and vectors
- Add your own functions into apply statements
- Nest apply(), lapply() and sapply() functions within each other
- Use the which.max() and which.min() functions
Skills you will learn!
Curriculum
7 Topics
Welcome Challenge!
Welcome to the Advanced R Programming Course!
Learning Paths
Extra: Interview with Hadley Wickham
Get the materials
Your Shortcut To Becoming A Better Data Scientist!
Study Tips For Success
22 Topics
Welcome to this section. This is what you will learn!
Project Brief: Financial Review
Import Data into R
What are Factors (Refresher)
The Factor Variable Trap
FVT Example
gsub() and sub()
Dealing with Missing Data
What is an NA?
An Elegant Way To Locate Missing Data
Data Filters: which() for Non-Missing Data
Data Filters: is.na() for Missing Data
Removing records with missing data
Reseting the dataframe index
Replacing Missing Data: Factual Analysis Method
Replacing Missing Data: Median Imputation Method (Part 1)
Replacing Missing Data: Median Imputation Method (Part 2)
Replacing Missing Data: Median Imputation Method (Part 3)
Replacing Missing Data: Deriving Values Method
Visualizing results
Section Recap
Data Preparation
12 Topics
Welcome to this section. This is what you will learn!
Project Brief: Machine Utilization
Import Data Into R
Handling Date-Times in R
R programming: What is a List?
Naming components of a list
Extracting components lists: [] vs [[]] vs $
Adding and deleting components
Subsetting a list
Creating A Timeseries Plot
Section Recap
Lists in R
15 Topics
Welcome to this section. This is what you will learn!
Project Brief: Weather Patterns
Import Data into R
R programming: What is the Apply family?
Using apply()
Recreating the apply function with loops (advanced topic)
Using lapply()
Combining lapply() with []
Adding your own functions
Using sapply()
Nesting apply() functions
which.max() and which.min() (advanced topic)
Section Recap
"Apply" Family of Functions
THANK YOU Video
2 Topics
Huge Congrats for completing the challenge!
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R Programming: Advanced Analytics In R For Data Science
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